Nearly all data shown here is from the South Africa National Institue for Communicable Diseases (NICD), but it is accessed through different channels. Cases, deaths, and testing data are retrieved from Our World in Data on GitHub via Johns Hopkins and NICD. Hospitalization data and provincial data are retrieved from from the Data Science for Social Impact Research Group @ University of Pretoria, Coronavirus COVID-19 (2019-nCoV) Data Repository for South Africa. Available on: https://github.com/dsfsi/covid19za. Many thanks to all who have worked to collect this data and make it publicly accessible.

I display data since the beginning of 2021. Dashed lines indicate the date (Nov 25, 2021) when the Omicron variant was announced by NICD. My processing and analysis code can be found here.

Cases

The line chart below shows the weekly growth multiplier of seven-day average cases. Values over 1 indicate case growth, while values under 1 mean case decline. For example, a 2.0 growth multiplier would mean cases are twice as high as the week before (rising); 0.5 would mean that they are only half as high (falling). Dots show daily values compared to seven days earlier.

Deaths

Percentage of peak values This charts display the 7-day average for deaths (black) and cases (orange) over time, expressed as the percentage of the all-time high values reached in summer 2021. Deaths are lagged by 17 days, the observed gap between the peak of cases and the peak of deaths for South Africa as a whole during the summer of 2021 (Delta wave). It is designed to explore differences in disease severity over time.

Case fatality rate This chart displays the 7-day average for deaths (lagged 17 days) divided by the 7-day average for cases. The lag reflects the observed gap between the peak of cases and the peak of deaths during the summer of 2021 (Delta wave). The chart includes a loess smoothing.

Testing and Positivity

Positive rate reflects the 7-day average for new reported cases divided by the 7-day average for new reported tests. When data from JHU/Our World in Data lags reported data, I instead use figures from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub that include data on cumulative tests from NICD press releases. Provincial weekly positive rates are also from NSFSI.

The chart below displays weekly positivity rates for South Africa and Gauteng province reported by NICD and catalogued by DSFSI. Past weeks may be updated as more test results are reported.

Hospitals

Data from Data Science for Social Impact Research Group (DSFSI) @ University of Pretoria via GitHub. Presented first for South Africa as a whole and then for Gauteng Province specifically. DSFSI catalogs hospitalization data reported by NICD’s daily DATCOV hospital surveillance reports.

Percentage of Peak Values Case and hospitalization metrics (seven-day averages) over time as percentage of peak values for South Africa. No lags are applied. The gray area chart shows the progression of cases over time, while the lines show hospitalization metrics.

Data Table (JHU)

var date total weekday new avg_7day
cases 2022-01-01 3468079 Saturday 9793 8591.71429
cases 2022-01-02 3472436 Sunday 4357 8413.71429
cases 2022-01-03 3475512 Monday 3076 8313.42857
cases 2022-01-04 3483590 Tuesday 8078 8436.57143
cases 2022-01-05 3494696 Wednesday 11106 8734.57143
cases 2022-01-06 3504554 Thursday 9858 8288.85714
cases 2022-01-07 3513813 Friday 9259 7932.42857
cases 2022-01-08 3521572 Saturday 7759 7641.85714
deaths 2022-01-01 91198 Saturday 53 60.71429
deaths 2022-01-02 91228 Sunday 30 59.14286
deaths 2022-01-03 91312 Monday 84 69.00000
deaths 2022-01-04 91451 Tuesday 139 85.28571
deaths 2022-01-05 91561 Wednesday 110 89.42857
deaths 2022-01-06 92112 Thursday 551 150.14286
deaths 2022-01-07 92252 Friday 140 158.14286
deaths 2022-01-08 92371 Saturday 119 167.57143





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